{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [简单的折线图](04.01-Simple-Line-Plots.ipynb) | [目录](Index.ipynb) | [误差可视化](04.03-Errorbars.ipynb) >\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/wangyingsm/Python-Data-Science-Handbook/blob/master/notebooks/04.02-Simple-Scatter-Plots.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simple Scatter Plots\n",
    "\n",
    "# 简单散点图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Another commonly used plot type is the simple scatter plot, a close cousin of the line plot.\n",
    "Instead of points being joined by line segments, here the points are represented individually with a dot, circle, or other shape.\n",
    "We’ll start by setting up the notebook for plotting and importing the functions we will use:\n",
    "\n",
    "另一种常用的图表类型是简单散点图，它是折线图的近亲。不像折线图，图中的点连接起来组成连线，散点图中的点都是独立分布的点状、圆圈或其他形状。本节开始我们也是首先将需要用到的图表工具和函数导入到notebook中："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter Plots with ``plt.plot``\n",
    "\n",
    "## 使用 `plt.plot` 绘制散点图\n",
    "\n",
    "> In the previous section we looked at ``plt.plot``/``ax.plot`` to produce line plots.\n",
    "It turns out that this same function can produce scatter plots as well:\n",
    "\n",
    "在上一节中，我们介绍了`plt.plot`/`ax.plot`方法绘制折线图。这两个方法也可以同样用来绘制散点图："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, 10, 30)\n",
    "y = np.sin(x)\n",
    "\n",
    "plt.plot(x, y, 'o', color='black');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The third argument in the function call is a character that represents the type of symbol used for the plotting. Just as you can specify options such as ``'-'``, ``'--'`` to control the line style, the marker style has its own set of short string codes. The full list of available symbols can be seen in the documentation of ``plt.plot``, or in Matplotlib's online documentation. Most of the possibilities are fairly intuitive, and we'll show a number of the more common ones here:\n",
    "\n",
    "传递给函数的第三个参数是使用一个字符代表的图表绘制点的类型。就像你可以使用`'-'`或`'--'`来控制线条的风格那样，点的类型风格也可以使用短字符串代码来表示。所有可用的符号可以通过`plt.plot`文档或Matplotlib在线文档进行查阅。大多数的代码都是非常直观的，我们使用下面的例子可以展示那些最通用的符号："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rng = np.random.RandomState(0)\n",
    "for marker in ['o', '.', ',', 'x', '+', 'v', '^', '<', '>', 's', 'd']:\n",
    "    plt.plot(rng.rand(5), rng.rand(5), marker,\n",
    "             label=\"marker='{0}'\".format(marker))\n",
    "plt.legend(numpoints=1)\n",
    "plt.xlim(0, 1.8);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> For even more possibilities, these character codes can be used together with line and color codes to plot points along with a line connecting them:\n",
    "\n",
    "而且这些符号代码可以和线条、颜色代码一起使用，这会在折线图的基础上绘制出散点："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y, '-ok');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Additional keyword arguments to ``plt.plot`` specify a wide range of properties of the lines and markers:\n",
    "\n",
    "`plt.plot`还有很多额外的关键字参数用来指定广泛的线条和点的属性："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y, '-p', color='gray',\n",
    "         markersize=15, linewidth=4,\n",
    "         markerfacecolor='white',\n",
    "         markeredgecolor='gray',\n",
    "         markeredgewidth=2)\n",
    "plt.ylim(-1.2, 1.2);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> This type of flexibility in the ``plt.plot`` function allows for a wide variety of possible visualization options.\n",
    "For a full description of the options available, refer to the ``plt.plot`` documentation.\n",
    "\n",
    "`plt.plot`函数的这种灵活性提供了很多的可视化选择。查阅`plt.plot`帮助文档获得完整的选项说明。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter Plots with ``plt.scatter``\n",
    "\n",
    "## 使用`plt.scatter`绘制散点图\n",
    "\n",
    "> A second, more powerful method of creating scatter plots is the ``plt.scatter`` function, which can be used very similarly to the ``plt.plot`` function:\n",
    "\n",
    "第二种更强大的绘制散点图的方法是使用`plt.scatter`函数，它的使用方法和`plt.plot`类似："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x, y, marker='o');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The primary difference of ``plt.scatter`` from ``plt.plot`` is that it can be used to create scatter plots where the properties of each individual point (size, face color, edge color, etc.) can be individually controlled or mapped to data.\n",
    "\n",
    "`plt.scatter`和`plt.plot`的主要区别在于，`plt.scatter`可以针对每个点设置不同属性（大小、填充颜色、边缘颜色等），还可以通过数据集合对这些属性进行设置。\n",
    "\n",
    "> Let's show this by creating a random scatter plot with points of many colors and sizes.\n",
    "In order to better see the overlapping results, we'll also use the ``alpha`` keyword to adjust the transparency level:\n",
    "\n",
    "让我们通过一个随机值数据集绘制不同颜色和大小的散点图来说明。为了更好的查看重叠的结果，我们还使用了`alpha`关键字参数对点的透明度进行了调整："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rng = np.random.RandomState(0)\n",
    "x = rng.randn(100)\n",
    "y = rng.randn(100)\n",
    "colors = rng.rand(100)\n",
    "sizes = 1000 * rng.rand(100)\n",
    "\n",
    "plt.scatter(x, y, c=colors, s=sizes, alpha=0.3,\n",
    "            cmap='viridis')\n",
    "plt.colorbar();  # 显示颜色对比条"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Notice that the color argument is automatically mapped to a color scale (shown here by the ``colorbar()`` command), and that the size argument is given in pixels.\n",
    "In this way, the color and size of points can be used to convey information in the visualization, in order to visualize multidimensional data.\n",
    "\n",
    "注意图表右边有一个颜色对比条（这里通过`colormap()`函数输出），图表中的点大小的单位是像素。使用这种方法，散点的颜色和大小都能用来展示数据信息，在希望展示多个维度数据集合的情况下很直观。\n",
    "\n",
    "> For example, we might use the Iris data from Scikit-Learn, where each sample is one of three types of flowers that has had the size of its petals and sepals carefully measured:\n",
    "\n",
    "例如，当我们使用Scikit-learn中的鸢尾花数据集，里面的每个样本都是三种鸢尾花中的其中一种，并带有仔细测量的花瓣和花萼的尺寸数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IZtNuT9uEQ4SwUgEy3U5vDM5re94V6nnGOPznFMu2OPfyGY6dHyNqR7gZBy+7e9EnIcQjs268rPdY+zYY7qOTRUhmQA50Gfv20Ki065T7+T1JWTwKGIf/nJPJeUY73nCoeSA73N1E532EyKL1EugyiL59tPD5oSuH7/v+C8DJzsvJIAhu7p9Jh5+wHTHx6RTVlRrFvgInLx3fl6wUpRStehvHs7fMqlmZLzP12TR379ylPz/I8MntH4UNhkOLroGqdd0QZCMZdDyFcI3D74ZtHb7v+xbw94H/FpgDpjqrjvu+PwL878A/CYLg2dHa3QOUUtz83m1a9TbZYobyQoV2o73nomRJkvDZu7epLteQtuTS589viI9XV2rceOcm+Z4cwrK4+9E4lm0xeOzJswUMhqeJVmV43FxzkQO9gtbJnuarP6/sNML/d52fV4Ig2CDt5vt+DvhvgG8CP7J/5h0+wlZEvdxYyz0v9OUpL1QIW9Gehk7q5QaVpSq9wz00Kg3m7i1y7uUHDr+8UMHxHNyMi+1YZItZlmdXjcM3PIOEpKmXu0cIgVYaSB57H0eJnRz+fxUEwdRWKzo3gH/q+/5v749Zh5f7Oez3c9yVUmhN17nt3XI/lz1sRYTNCC+7MTvGdi2S6EEDkLSYylzwhmcRyVqF6uNgophds63Dv+/sfd//WeBngBKdol9AB0FwLgiC6adi5SHC9RyOXxhj/NMppCVRieLUPsTwc8UsZ18+zfz4IiNnhhg9uzGdcuBYP/MTS6zMlakt1xjqG2L07Mie2mAwPB0y8Dga89DpIGVh8k+6o5uz9HeB/4Q0jm8Ajr0wSqEvT9hK0x1L/cV9Oc5OksCO6/DiFy9SWaqiCzFXv3BpU468wfAsIKx+dCLQWu0+vVJXwTph0jK7pBuH/x2gEQRBfb+NeZbYLye/G5TSCCmRUjxTjckNhvUI4aLlKKiltJK2S7TWoGOEZZ5su6Ubh/8hcM/3/TkgZl1IZ6eNOhO7vw6MABngHwRB8I11698D1tdG/+R2cwaGjWitmb41y9Rns7QabSYnJtEVi5HTg5y8dHyDdELYCtMnkaxrBM0MhxZhHUOrWbSOEKLL61StgHWsqyYihpRuHP7fAa4AM7vc918H3gmC4B/5vn8a+APgG+vfEATBV3e5TwMwd3ee2x/eo7Zco90MqSxUWMguErZCAM5cOYXWmnvXJ7j2rRu0Gi2y+QwvfeXFTS0Ol2aWWZxaxrItxs6NkC/l9tze6kqNmTvzjN+eYmzw2K70+A1HAyELaPsKRB+hZREhds5408lyqpxpv/CULHw+6Mbhvw0s7jakEwTBb617eRKYfOgtBx8TeQZJkoSpm3M0Kg2iKKHQlyfbzCGkTFM1Mw5j50aoV5q88+8/wLYsMvkszVqL7/z++xT7i/QMpKd+cXqZm+/dIVfKouKE629XuPKWTza/dyqE9XKd629/hpdzCZsRn37nJpffvEDPYGnPjmF4PpDWAFrcbwpS26IpSNKRIU7AGkbY503u/S7pxuG/QBrSucXGkM4XujmA7/vfAk4AP/rQqgHf938DOAP8EfALQRCYQPQjaNXbhK2QZrVFoe9BXr6Xdaiu1AibEY1qi5nbs8RRTG+nXsD1CixMLrIwsbDm8BfGF8n35NZSPsuLFSpL1T11+Euzq9iulQqvZV2yhQyLU8vPtcPXWgFiy6rnVPM8Aq1B2Km0rmENIXvBfROdrICa6DQFEYAGIdMQjhxBSCPS9zh04/D/1pMcIAiCL/u+/yrwb3zff2WdU/854DeAJvB7wI8DX394++vXrz/J4Z+IVqt1oMffila9zeTkJMtzK5QbGRCCOAqZmp6hWW7S0k0yn9ksT60wPztHW7dSTXGlWZ0rc/feOJHXBuDu+CRRK8bLpU6ntlwjctss1xf3zN6Fu0ssTq2Q780Rhm3u3LlLsT9P6Db37Bh7zeP83wUNHLmMLRdIm1gIlHaJ1CiJ7kESYcslbLm4th40SueJ1AiJLrHbwqHDeH1uxePbmQVsROd8aSygDYzvqX33ef7PZ3cO/wzphOrPAPi+/zvAPwHu7bSR7/tvAPNBEEwEQfC+7/s2MATMAwRB8C/WvfcbwMts4fAvX77c3SfZB65fv36gx98KpRSiZrNYXKK2UifXk2NmZoa+Yh/Dg0MMHOvntc9fpX0lpL2YELZCvKxHs97i/OUBvvyDb67p1Z8YPcn1tz/DsiVKaUaHRrn8xYt7Orl79nTaSzeJFRMTE5w6dYoXv3Tx0Gnm18OQuXqNWjtk4u4drp45zXC+QMHdeQSudYiOPgVVS8v8xZW1MIPWISRlSD4EkQFrDOTohhTCB71LawjnCkJ2H+k8jNfnVhg795Zu7Hz33Xe3XN6Nw/8VNo7yfxb4HeCtR2z3FeA08Pc72jsFYBHA9/1B4F8DPxYEQQR8P3DkqnYfByklpy6foFFpIoDqaoNmpcXYqEuulOPkxWM4roPjOnz5xz7HtW9/RmO1Tt+5Ea68dWmDoy32Fbjyls/qfBnLlvSP9e95Jk8m53Hlyz7Ls6u0RJ0rX75ItnB4nH07jgkWF1lqNbClhWdZtJViulplvLxKbybLpcFBMvbm85Jqsn/QSQ3cql7CAr2SxpyppTnjbAxlCZEFK4vWLXT0PjivIOTzG+4yHCzdOHwrCIJb614vdLnvfwn8H77v/ynps9nfBX7K9/1yEAS/6/v+HwHf9n2/DbzHFqN7w9b0j/Ry6c0LTHw6RaGvRWS1GToxwLELYwyfHFx739DxQb7vx/uJowTHtbeMKedLuX3JzFmPl/UYOzvCamv50Dn792dnSLRiMLduPsSy6Mmk8xiVdpv3ZmZ4bWxsg9PXWqGja6CT7R10MpWO/O3eNGafzKb9Tq2hTW8VIoNGoKOPOvruJrZv2Hu6cfhf933/beAvSEUv3gL+z0dtFARBE/gvd1j/q8CvdmnnoSNJkrVK225bBu4l/SO9eFmX+XuLVNplzr16llL/Zj1xKSWut39ViEmSUFmqUV+t42Zdeod7npl8/2BxkUQrit72k9Qlz6PabnN9YYHXxtaltOpVUJVtJX3TcM4i3L8ZCAGy055P9m+ZXSKEh1YNdDKPsE9sWm8wPCk7ySPbQRDEnTz63wFeI5Wk+1+DILi3/j1PydZDw/zEAhOfzqCStCn4qcvHGDox+OgN19GoNfn0Lz6jWWsx9sII566e3tX2s/fmU/nk1Tqz09O4cYYzL53i1KXja04/SRIWJpdo1Vrke3IMHOvfJOFcXamxPLOCZVsMnRzYVeeqKIy48c5tpm/PEbdDhJT0j/by4pf8R3bJOmjqYchSq7FhZL8dRc9jqdGg2m5T9NLzo+PJNC6/HWo5zSpZfwMWdjri1zUQ29QiyAIkk2mjbiMXYNhjdpRH9n3/F4Ig+ItOw5MNTU98338T+CXgr+yngYeN6kqNOx9NUBooYNkWSZxw+4N7ZPIZin3d9dYMWyH/4d/8CZXlGl7G4bP37tAoN7j6VncTRs16i8/evcPi9DJSSuJIsTS7jNaavuEeSgNFtNbc+WicpZkVvKzL3L1FaqsNzl49tbaf1YUywXdv4mZcVKJYmFziypf9rjV5Zu/MM3Fjinq5iZexicOQufFFbM/mtR946VA3Y5mr17Bl909mjiWZq9Uoeh5at0Gt7NywI1mErSpApQdqEeTWDl8IB60rnZuCieUb9padHP5PA/+b7/tngT8jLZzSpDn13wfcBf7rfbbv0LE8s5KGcezUWVi2hZNxWJ5Z6drhz91bYHWhzFhH3TLXk+OTPw+6dviVpSqNSgMpBPlSFq/m4WU8auU6y7MrlAaKtJshy7Or9A2njiVbyLAwscTxC2NrIZfZO/Nki9k1Hf/yYpXVhcqGeYCdmB9fpFFuUuzLI6WAPFRX6tSW6zSqzX2fG3gSau0QbxehOM+yqUVpOmuq7Lj9zSzVeAlBbuHwNaCqaNnoNP1wN98YtSQtedkfUt2lVmcyWYBwzJzBEWEneeRp4G/6vn8S+CpwP4D5PeAfB0GwP8mwh5ytRMqE2L14mVjnMB5nJKy13hguIH15347U6ehN6x/ayaZ97uZzpDZsWvrcCrk99qfSGnQzzdhJVtJlQqU7lB5ajoEsIcT9r+P+nD+tQ3SyBPHNNOSkozTshIu2zyDsUyCKh/rJzPBkPHLSNgiCCbqYpD0q9I/1MXdvgUzeWwvptJsh/WPd99QcOjlAoS/P/MQSmZxHbaXGa197qevtS/0F8j05WvU2jWqLsBHS8tr0j/bSP5rakcl59I30sjK3ipfzaNXbDJ8c2DChOnJ2hBvv3EIliiROsB1rrTK3G4ZPDbI4uUx1pU4m66ZNWBybYn+BbGHvqnX3g4LnUqm2yTrdTTC3k5hhrxPvf4S4lxACLRzQnVF6MgWqicKiEVq0lUUkBY60KLrgqrsgJNo6i7B6OjfRvU0EUMkShO+kmUI6BukCMr0ZqSqo2TTryD4P7lUz4n9OMV0Ddkmpv8jpKyeZDKbRSiOk4OxLp3Yll5zJZfjhn/oq1/78U+q1Fle/7xLnXz3b9fbZQpbzr55Fa01ttY60YGC0j9NXTlLqyCYIITj3ymnm7+VpVJuMnBnaFKrpG+7h0hfOs9SZtB0+NbirSduxcyOUF6vM3p0nbEa4WY/+kRIXXj+3p/1994PhfIF75dWu3x8mCSOF++fWQ8tetGog5DZhK2sI4sl0VK8iVkKLcquFpEHMMRIRoXSb+Zqm4HmM5h0cfQutT6XOeBcywY9CxQsQ/ikky2mmkPXQzViShnd0HaL30bTBfcM4/ecQ4/Afg9HTwwwe6ydqRzies9aOcDfkSzk+/1dff2wbxs6NUBoosDJXJnvT5fUvvkyxb2NapmWlCpg70TNYemxdm/tNWI69MEpttY6Xdegb6d3VTeOgKLgufZkslXabkrezvbWwTY+Xobiu6lbYJ9Dhx8A2Dl/0QfJd0BYLLU0tapJxLCxc2hRweHBDbEUxd1cjTvUU8PR18H5ozzJ0tKpB+O00lGT1b98sXFjpJLGqQ/QpWmQR7qt7YoPh8PBIT+X7/glSnZse1kVsgyD45X2069BjO/ZjOfq9JN+TJ9+Tp5qUt3zCqCxVmbs3T6PaIlfKMXpmqOuJ5W6xbIuBsT4GdhHSOixcGhzkvZmZDemWD1ML2yituTw4tDG2LXpBFtCquo0cQgI4VKIG1UiQcxws6kQMARudecaxCZOY6coqZ0oSsYcTtjqeADW3s7Nfj8xDEkN8C22fR8i9vV4MB0s3w4j/GxgFloGldT+GAyZsR8xPLLI0tUKz3tqwbvbOHNfevkGj2sLxbOrlOte+FbAwuXfCaM86GdvhtbExsrbNUqNBpd2iHceESUKl3WKxUcezbF4fPbYp1i+ERDgvAhKtqpt3rhbRcpjVFuSdEIsGMb0kbJ2O6VmaWDWo6gugF9PCrSdE6xDiW6DdtAagW2QekhV0vNsWGIbDTjdXwXIQBD+375YYdkXYCrn29g2iVsT85BKfqGBNlKzdbDP+6TS9QyWkld7TbcfGy7rc/WSS3uEeHPfZqIbdb1Knf4xqu81crUYtamMJwXAuz0ihSNHdIm2ygxAZcF9FR9fT7BfhgsgDGtQS9diipfNkqKBwUFuEfwQRkhYam9g6z1xTUvI0qPKWEgy7IZUYXgJrl3IWwgZsSG6h9TmjOf8csVOl7YudP//c9/3/jjQXf+1ZMwiCa/tsm2EHlqZXiMOYnqESy7Ucli2ZvTPPuZdPU12upZX81sYHOMu2QGtqK3X6RnoPyPLDSdHz1sI63kqZCwPd1SII4YHzCugaOplNs2AIQVUIkwyxOE6VV5DEWCxjsdqJi6apl4oMISdJKOJYkmrYBpFD6/YOmf7d0k7rAR6n15CQnW0T9jpjyHBw7DTC/2cPvf6b6/7WwA/uvTmGbkniBCHXTdDaFnGcpOsStUXSfYrWGqWezzz5g0IIkeavyyLaPo/WddA27dAhoo4nXBQuihyRHkWQ0EnCR+M8+F/pEIcliOcgqaJUp42fHNqQDaR1E3SEFDW0bu7Q0/Ux/s9ag26l1cCqgg7fR4s8yGGElWoAad1GJ4tpLj8RYKd2WsOmv+whZ+nypCkAACAASURBVKfCqx8A8H3/80EQfHf9Ot/3f2C/DTPsTN9ID9O3Z2nWWoStiEa1yQk/rY0r9ObRSm3a5n5BVK50tL6UrThirlZnrlYlVApLCAZyOUYLxUdm6OyWNPzjoiVkbHvzzVXY6Ie/djrGYQ70EpZQtEKXWPQhkhoZawVL3kGLfrAHIFlIwz1CkLEm0O0GWvalYmui96HsHrczUdvlKD2pg54D3YaklR4P0nTN+Bo67tTqCUXq5DOk04AhJBPo+C5aDiDscwiZQ2lNlCREKkFpjdxFQVdaJLYAagZUO/0csgdhHQNRMsVhj8lOIZ3zwEXgV3zf/x/XrfKAf0zaGMVwQOR78lx+8yLTt2axbMnFN87R3wnT5Es5+sf6UrmH/geaP9XlGsOnBve0heFhRmnNreUlpioVpBQUXA/XttHAQqPOVLVCXybL5cEhPHvvMq6EcNGij4JbB8GOzk7oEJe7aN1mtga2tElqDepak+g0NDeS9RjKfIgTVsC5sqbhk+gywhpA6yY6/BDkIDg+olMYJmQJLfvSwirrETn1SRnUdCoIJ3IgY7COrz1ZaG1DdB1UBaxjYJ/ZGNsXmU51d5Ww9Q6zrbNM1hSxUowvL1GdHOdUqZfhfH7Hc621RicTkNztPAQV0h8AVUaruTR91L60fQ2EYVt2usqzwOeBYTaGcxTwi/tok6FLin0F/M+dR+WjtQrb+5x96RTZfIbZuwuoJMGyLU76xxk582QTgc8KSmuCxQXmajX6c7lNI8JSRxK50mrxwdwMr4yM7a3Tt0/gqI8ZzOVYajS2lmDWCS73UKrNVC2h1g4535/HtgYRHTVNpSNqrYBWq8WxniEyySRaFtK5g/vHut9ERa2io+vpTUFYCJlHW6dAvQdadWQUtiCpgZpKJ5yFlebii2zaoYtOH974JiDBPpY+YSSTYG9UeBVCUAkdbi8vInmHgvsaUuYoux5Z2+HO6jLj5VVeGhnd9slKx7chGQfZj3hY3E4UgAJa1TvNYl41Tn+X7BTS+Qj4yPf93w6C4JOnaJNhD7Asi+MXxhg9N0wSJViOdSC6/QfFTLXKTL3GUH5n+eNSJkO51eKz5SWuDu9cpLYrRC8Ij7E8VNsWjbBNzt3o5CzKCJpM1zS1dsjJ3hKe1abNgwljhzKeE9FIikyUm5ztdbCTObBPPXxEhOxFJ4voZA5hp+E94ZxFxzfTEby9Ra2E1mmevsilzl5H6WStdbqTcQSoVVAtsDoppaKUpp2qjXMLjTDk5tIyWbdARtSImCPsBAIcy6Ivm6MVx3wwO8Mbx46TeyjVVavljrMf2LHwTMh86vTjT8F5zYR3dsFOIZ0FOrM+vu8PkDYbl6QhnckgCHYn4H6IaNZbNKtN7I7uy+NcMLXVOmErxM24W2q/11ZrLEwuI4Tg2AsjXUsO74YkTqgs16gs1gjb0ZaNRyzraDl6SEf34+VVendobLKekuex2KjTjKKutXUehRASnCvY0Xtc6C9wZ7VBudXElhYZx05bcqtZFluaajvkVG+JkhMSMoISnRCGVjgsEpPDtSzqSZtq6NEnl9B6bOsDy540nm6lvXOF7Ee7b0D7bYhXwSpuLMDqTAAjvdTRqzpYw+BeQgiJ1irNPJLrrnEhQNtpyuc6hz9Tq2FbEkdaJLqIwzyh3tjIJWPbtOOYqcrmTCgdT4AsdFVlLGQ+TYXVle17Cxg2sdMIfwjA9/1fA34rCILvdF5/GfiJp2Pe3lNerHDjnbRjo1KaweN9nHv5zK6c/uydOe5dn0JKgVKa05ePM3r2wehwZX6VP/hXf0y7HYHS9I308LW/9RUyub2LncdRTPDdm9TKDSbHp3HjLJffvLAmdXyUSQuoEgpdTsgKIbBEqnd/pm/vKoaFLIDzKk70IRf7bBpxnoVmm2orRFAna4d4VoYzvZKC0yZklJjhte0lDSQRivS6yTgOi402fZ6dxuW3/Cz39fQr6VMGIOwX0MhUPE0tpYVYVhaQoBaAGOJyWkdvHQPn6oNsG90Aws3a/iIHyWKnUYtFmMSstloPQjVCglbYerNeUdHzmK5VOd3bh9sZjGhVB7W6c4+BTR/WRSfTiG16Cxg2002l7ev3nT1AEATfAl7eP5P2l7sfT5AtZukZKtE30sPi1Aq11XrX24ftiPFPp+kZLNIzVKJnsMhEME0URmvv+fA/XkNIGDszxNi5YVbmy9z+aG/VpBenlmhUmvQN91AYKKCVYub27J4e41mlHkWpPv8uyDgOlXZ7z20RsohwPwf2aXJOzOmi5uqwy5VBwYm8xJY1PKdEi/PEYmRDOq2ghV6XjW8JSaIVkRbpyHz7o6LVg8prIQTSOYfI/mVw3wTZl4ZoVBmSVcABezRd5762MS6uo62zO4XsCK2k130zihFio9S3xkbQ2rSpFAI0NKNo3dI2O/UY2PpjZtNJZEPXdDNLteD7/teBb5FO2H4e6F5m8JARhRGZwoORn5SCpJO/3g0qUSD0WlHT/d9JrHA6UZtmvYW7TkDMdm2i5pOXyq8nDtO4/PpjhK1ohy2ODlrpXYfpBJCwOZV1LxDCQ9in0NbxdOStozTlUAtqCoq77mwlYEdbt14vZC84ObQoQXIvzbfX7TQkYp/paPJvapqw2Q/rJHW0yQxolebpJznQD79RILarBRCg16/Tit3XDTzqPBgephuH/18Afxl4kTSZ998Cv7+fRu0ng8f7mR9fpNhfIGrHSEuSK3afl+5mHPKlHLXVOrlilnqlQa6Uxc08iP2evHScd/79+zieTRwlRK2I0bPDO+x195QGi0zdnCVqRyRxQqPSfKQy5lHBtS2U6v4mDhApRd7Z33RVIaxURRPSfHy1iG1FxCpJ2y3qGItKZzI3RtBAUiUhw9pXVYDUGnDYtiuWVmupmWuLdISO76Z57RoinSGMs1iqhCcjRHITlIeWJxDW+ips64Ef1kkaz08mIakQ6RAd1RGWjasi+oVA6vOEnEy7aJGgsLe0U2s2tpgUNrse4ROnchaGrtlp0vbHgiD4PeDvdBbdf4Y8CfwM8M/32bZ94aR/HCFlp1WhywuvnN7VhKqUkguvn+PetQlqq41UH//FExv03y+/eYG4HfHZe3ewbIu/9DfeZOT0Hjv8/iIXXj+bhpPaMWe+eILBY/17eoxnlR4vA6RdyLod6bfjiJGBXcSPnxRRACEZyeeYrFTp92pp8RWgcNFIBA42K0haJJSoxb2UXBfLUqmuPZvDOmud0GRp3bIQHX0Mqs5yy+XmyjIT5WmUVmRklWFvhbHeY4zmwVU30ZxCWJ3rVWY7A+kI4gAdT9NKXKqxJE482niAwMIhTGqUko9wrHka+tXU4YsSPBTWaccxedchv36CXKSTyVon3Wv3qBrYF7s/54YdR/j3b/NDpPf45yL3ybItTl8+wenLJx795m1wMy4XXn9h2/VSSl756lVe+erVxz5GN/SP9tE/2oc7LBk9Y0b39/Fsm+F8nuX1k4g7ECUJrmXTm3l6FchCuGg5Sq87w4KYQeoKiSixflpNYRMzhKSJpIqtG/S5Zx8UR22FroEcWsvT1zpJc/NVg+tLER/OT+BaFr3ZLJYUCDIIvUqwsMC9FZfXxkYpMJHG363+ThFZL4TfI0kWWYk82pHCtWJsZxBJOlhKtCIMHcZrmlOlRfLiu9R5HSU2ZrBpram021wdGt5wMxbCQlsn0rRM8eiJc60TEHJ3k7yG7SdtgyD4V50/v0IaKPtD4H8KguCXgiD4padhnMHwuJzu7SNRina8s7Z8ohTlVosL/QO7Kv3fC4Q1hsMyJ/NNqqFHskUIO6EPTUIjlPS6mqycBTm25ZOL7uTQC+vkg2XJMqgVbq7GfDA3w0A2R1/H2UM6sarlIMP5dA7jezMztJJMmtqp78fHJcQTrEQuYaTxHIElHRQPbpCWkAzk83jSZrwisNUUqaTDus+iFEuNBseLxS3rI4Q1Brhpxs4OaK1ArYB11nTl2iXdZOn8JHAd+BvAH/i+/82HpBaOJK1Gm8pSlVbj8TM76pUGleUqYXvrydawFVJZrtKo7pSRYdiKnOPw6ugozShitdUieUhbSGtNLWyz0mziDww+skBrXxB5EC5F1+FkqUgYx9TCkDBJiLUiVgnVSFCJBhjIaPo80cmO2Wyr1s0068a5urFpiRon0hk+mptjIJvDtjZ/5Vt6kFh79HpJJ0e+3ml5WEtDRMkUbQaIojquoxDERAyylfsYLuYp2BELrV6icJxKu04tilhqNKi227zQ38/FgcEtb1hCuAjnpXRCV62kFb4Pf07VSFNL7TMI6/juzrehqybms77v/79AhTQ7503gR4Bf2WfbDi2L08vc+fDe2uuzL5/edfx8/NNJZu7MI4VE2oJLnz9PvufBF7m2Wue9/+8jVuZWcTMuL375Iqf8xw9DHUVKXobPHTvOTK3KZKWM0p32rQBoBnMFrgyV1mQWnjq6DFYe5HmKcpx8n0s9slltxyQqQUrJiOdRcF0cMkCSZrMkU2gxiqCNVpVO4VQB4b62ISddqxqoOlNViFSCY28dG9dY1NQJCnKG/kyFmfo8p3pP4SRzICUkc5TjAkJEWNRJGNgsAIdG0kaQUMgep5WUGM1WyYqIRibDlaEh+rI57Ef0OhYyB85r6GQGksk0dLP25KNB9iGcCwj57HVYOwx00+LwA2Ae+F3gG8A/DIJg73qwPWNEYcTdjyco9OXXRMnufDRO71Cp65aHtdU6M7fn6B3uQQhBq9HmzsfjXH3r8tp7rr19g9nbc2SLGerlBte/dYOBsX7yJaMdshuyjsO5vn5OlnqohSGJ1kiRPgFk7INtAqNVDbAQVi9aFpFqmaKYp+iwrho2ATkEcqCTd76cplKSQWsX5Eg6wSq2qhhPU4FnazW8R3xWjU1VHcdmgEY0SatdxfHKaaqlqtKIcjjOC9RxsWhgs0oa6X1wzJgeFCWQLkkSknEznMoomvkCQ/nuWyUK4SLs02lMX1fTpw0hgIzRznlCuvFQ/wvwZeCvAm8B3/F9/1sPSyYfFeIoQSuVNhMhnQTWWhFHSdcOP45ipJRrX1Av61Jdrq2t11qn0g+eQyafQQhJuxkSh0f2PvvEpFouh00WOuG+wxTCA2sMLUfS/Pj7eRLCRaxrT6hFFrQDcphYlzs3gvzW2UidGHyUJBuCLxYtbNFAdHLYEzxinUNjEZNjNT5GaB0DqwzyGFjDtHQvFulEcEyGmF4kManTlygsNkgw687PDtLMWsedm1faGwCR3TSRe79a+CB5lJ1a1dInLSJAIEQ+lXLeTVvJp0Q3IZ3fBH7T9/0cadOT/x74n4HD9u15KrgZBzfr0qy1yBYyNGstvKyH43X/z8105ImjdoTjOVSXa/QOP3gUF0IwfGqQ+clFass1oihm6OQQmfzhlE2IlSJRCiHEWqn8bomSB5rpzh5r/2itQJdJ4gUS1UIIO1WktPrW8tW1aqDVAqgGrryDinMIOYjoaMhoHacToHqlM+J00gyRTRr0u8Hl4cIhIeSWGThaR5DMQfs9YAnaBfq9Faj9adp0xX0NYZ/aOInZsSvruCw2GziiTkYsIGkQJppEgUDjWYK8tGnrPmKdoWQv4OkaJJ1GLWqenGggGESTI3XgEsUOE6YCpNCdz7hx8lareipznEylCflCdH57aOsUwhpIs4O0TrX4idMdiuxTnaTVqoZO5lLZ6PV2Sg8tTnbSVWdTmQshSc+L6mQQ2WjrOMI6dqgmlrsJ6fxz4DXS2uc/B34V+E/32a5Di2VZXHzjHDffv0t5oUKmkOH8q2d2JVCWyXlc/NwL3PrgHo1Kk56hEqdf3Biff+HVs2itmR9fJFvIcPmLF/Gyh8fh30+vm65WmK8/yKoouC6nenroz+awHhGvVVqz1GhwfXGByUqZRClsKTnT24s/MERfNh1JKa0pt1qESYIQkHNcCm53XyIVzdJo3WCxscJqS3Xy2zU591OGsyWK2WMIItCrnTCKixT1NH4c30WLHpAZwnCOlWaV5ZYm0RrHEgxmLEqZHhz3BaQ9uutzKGQJHT+6UlQnNWh/C+LbqbaNdQrsEokOAQHRBxB9hHau0rR/mLmGZr5eJ9EtBqxFim4Ptpolp+ustiW1UKARDxptaXBkzGjuBj2iQSYzSsEdBGsArBGI+ik5IdVoCs/JETKWdurahlYcU3QklnBSITZmOsfR6GQqlVoWDojSBgnktOn6Z+j4ZvqkwyqojmajANBoOYawxjZOTO8xa5r88Z30mnjYTtWC8E/Sm/+6/gQb95GkmU5qDuyrawOHg6abYenvAP9DEAQmVaRDtpDlpb90mSRJHluJsmewxGs/eBWl1Jb7cD2HF7/o43/+/KFTu1Rac3tlmYlKmYxtrzlmSL/sHy/M0+N5XBka2VZjPkoSvjs1yQdzs53mJC62JYkTxftzs3wwN8cXjh9nOF9gvFwmTGIQAqFBoenxMlzoH1jrQ7ulndE9plbfZ64ucew8Oc9Zs7Mdx9wqVxhufoPh/HGczEtrI3VNBiF70ieD6DrL9QnGm+dAFMg6DrYQaWOPaoSolTlTeo/ewmWkfWZX51HIPFr2oVVj29i0TprQ/uNUCkHm0wwda6AzonRS9UuKkDSoVP+C2fY0S+JHyDo9eKJApAYgCpB6hk9XshTcDK5tbSqqycpVWmGdspJcGYqBsCNT7KHtU+S9G6xEWRwULlOEnNhi4jYlTmL68xqsk51J5I7DTybSm5bs27K4Kq1NKEF8I72JOa9scKZpOuYiOplGO1eR+5SDr5Px1NlvYydqEUQbRD8k99Bi89zC/arqVLv/o1SjSBz8gO2Rz6JBEPyhcfZb86SOWAjxyH0cNmcPcGdlhfFKmYFsjoLrbYhnZmybwVyeZhzz8fwc8RatFpXWfGdqkndnp+nP5RjOF8g5LhnboeB5jBaK9GQ8vvlZwO/fvIFnWQzk8gxkc/Tncgzm8kQq4Xuz01Tam8W5AFSyxEz5A2YbLqVMiZzjbrDTs2DYm6cRF5itL6GiqS12MstSY4mFVpZBb4Gia2NLCykkrmVTymTJuTlurUKldh2VLO76XAr7DOhGGrLZivAjiO92KlFVWpS0RQhpuS1YDnP0e/cYcz/FsSwsKZGynz5vEdfuo9Ju047jTc7elQ2ysk4tcrBEBkfotIr1fmjJOo0jLPo9SSMEhcJmfktza2Gb/oxN1o7BeXntnGtV3dmJ0qkSTu6l8XJrOM1G0g+un1TuuZhWEUcfdeLme4tWlY6d/Vvamap6drpuyQwID+LbG+xcTzqyT1JZi0PA4wYfH4nv+znf9/8v3/f/2Pf9v/B9/0cfWv813/e/4/v+t33f/4X9smMrkjihUW0StvZW0Gw3NGtN7n06yY3v3WZhcpEk2az98ig7tdY06y3a9TZqC8e6HzSiiInKKoPZzV2k1lPyMlTDkIX65iKaxUaDD+dmGc4Xto35Kw1hkjBTrRJt8dlyjkvecfl4fn5Tjj1Aq3WTubqgx9vaTosagoiMU6AWZqi1pjY4Xa1j2u1p5hs2WbeIJMFis4OxhKTgZbhTSVDR7bW+wd0iZAmcq2nDcFXfsL1O6pB81gl/JOmI0ipu2kc7Tii3m3huAU2WDNeRutH5nC1Wwz5KbszF/gFqUZtyq0UzjogSRZgk6GSJagi9uRzn+gpUw6hTVJVed8LqBfdVetyIwYxFM7KJojJat9BoEq1oRCG1dpsBz2IkVwf3Cwj7+LrPMtOZgN5hAKNTiWRkKXWkOkwrhx8+Z8IBkdsXJ6qTKRDe9vMyaglwHtx0d7BzDVEENZuGrA6YnbR0XtxpwyAIrj1i338deCcIgn/k+/5p4A9I0zrv809J8/mngD/zff/rXezzkWitWZlbpd0I6R0ukS1snFtu1lvceOcWYTNEazh16dgGLfunQb3S4Nq3AuqVBlpr5u7OM3xqkItvvLDmnJq1JsE7t4g6CpinXjzByKkH7QmVUtz7ZIKFqWUmxifIqDznXzvbdabQ4zJXq2KtyzDaiaLnMV5eZbSwMWXw2sI8Qu48wbvSTNsC1tohE5UylwY3t2b0bJtaGLLaajGQe/BIrVWNleYCyMw22Ssam4UNOvMr7Rql3ArYHQ0ZVaEatrCk09Fk9LBZJNF9GySMIRUBS7RLtbVMr1tNR3+7QFqDaPE6Or4HaimVRBYCotvpaNIa7jj7rePAtbCNRHbszOOwiKvv0eICNrPcqQ9RsCwGsss4Mo9jZamEIWGc4FkRw1mLgtdL1kmzhlbVceqxpJgsg91ptGKfBaAkPiDrRLRCm5WoTEv3I4VgwBOUvAhXJuC+hXBfWTv3gijV3X9Uxo1a3CiGJjOd5iubz6eQOXSyhFb1PYuPC8JOg/itc/y1jlKHLx6aPxBeOqG+hZ2QPploJdHJwoab4EGwk3f4Zzus06QZO9sSBMFvrXt5Epi8/8L3/XPAchAEE53X3wR+CHhih78wucjtD8axXYvp27NcfevShsnOOx/eQ2tNz1AJlSjGP52iOFB8qvnt0zdnqK7WWZkrY9sSISTSkoydG6HUn47gbn94DyHEmp33Ppmg1F9Yu4EtzawwP7FE73CJlVqe2kqa23/S398Laq5eI+90N2HqWhbVdotmHK+1swuThOlKhaK7fTwzUYpq2CbnuMS2zUx1a4cPaQhprl7b5PBXm21yztYTe4IYSZuEdL0tLZqhJEpWcdcc/irllsLr3EA1DhZVBPGWE5aebbHSatNTqKaj9l2S6uZf7VTMVtNUQDEOchjskztuW2uHuB2p7DSuLrCYQ3KaWMVECkJ7lGpSxLLmyTktTpbygEw1erDQKGJ6SehBCqiGimKmCqQOXwgBzjm0HMJJJnDkTYruKth5UgnlHNivgHMRIQceSq9Mte53ymbSWqdyCWLdE4zwICmjtdp6WyHRqrp3Dl+004+yXS8FHXYyMx+yRXjpE9pOYn3CS28mHFKHHwTBD2y3zvf9n+/2AL7vfws4AawP6YwCC+tezwJbqpFdv36920MBMHltmmY9xMu51Fbq6HxMvveBM7j+8Wfke3OIxfQfU1uqQ0lR7N980bRarV0fvxuCD26xMltBRQl2xqFZblILK1i9mp7hElprPv34JoX+/NpZqq3UER8qCn2pnfN3F1mZq7DaWCEM28wszLJYWaC2zw0hbi8tkrPtrnVnKmGbYrVOzrZptVpcu36diZlpXCm3HeHHSrFUr9G0bVpJQg24peWWx2wnCY4QiMWltWW2mGO6No8j21uX8BPR4y4SqwdTU624hddKnXnYDpmemmO+voxnNRCdqLct61SiSZTefMOLEoVnNUjKnxLv0f+gaC/j2Q2U3jpeHscJ83PzrDQaZNZV0XqyynJ7koXWEAVnjqWqpNGZPI+THBnLpZ6XSBIcqfAsh3aSI00RXSFSippMELUGbbVVNk4WuIhNjZa6gEaidRaNR3rBLmx4d9T+/9l70xjJsuy+73fvW2OPjNxr66rq5XVVd0/PoiE5EmmREkFSMmFZsmUCHssLKAiWDcOEPwiwacomZVuA+MGyYMM2ARm2QVogbVISLBGwbJMAFxsmp9kznJ6ujt6qq7KW3DP2eOu9/nAjs3KJzIysyqzM7no/oNGVEZERJ15knHffuef8/0OW7t0nO9JGQ+Fbyyi918lL0idUdxlXfZaiS5JlpHrriOednCQesrT0gEy3x94vGeCOidHE0iPMPh0bJ2CmorVDpJ5dr+lZ8tIkbZl/HvgFYFs7wMWs1v+zSV6g2Wz+ySAIvgz8chAEbzebzXHKm4JD3A9u3bo17uZDma3N8/G7d7EsyVxjjje++voer1fRcxh0BpTrJbI0o1vq8dbbtw6UfsCcbE76+pPgRD5LHz5i83ELSwqm6w0aC3XefPuNHX9c0bUJ+xGlWpEszeiV+7z59i0Kox7++aktPnznU+pzVe7du89UaYrLry5y+ZVDvE5Pic6DJRzLmrhXfmMw4PblKxQchzt37vDya6/xvs7QaIqHXCkorRlsrFN0HPpxTNl1efnGzbGP7cUxU57P67O7yl3pFPHjdSxnBmvMylDoFJ/ezgofYBB3udRYxHGvc/fuXS5dvspwAyy7uPMcFj4lrqDFwSQ4TBKq9oBrM7eQIwPx/WidgWqhdQ+TXL3RLMD4kRY1+BTilSdlpn2sra4yOzdHuNXCcaydE5NDjOAKRXEDO2vTcjLKnjnWUZpStF1mq+YqxNItXB7sORZRmlKyNYvVKYRz45D3EgMa6X5t7P27aX7Q4+pLLx0ph6C1hqTDtmz06EZjtOLeGLvC19kaiDmwtk9W/uh4Pp1URvODLlevXkVY42VStOpDEoO17wpuJ86bh67wtRqAKCPdZ88nk+Sld955Z+ztkxR8/1PgLwP/E/AXMSJq4w01dxEEwdeA1WazudRsNr8dBIGNkVpexdTtdzcuX2a7d+sZmbnUwPUd4jChMlU6YOx9482rfPzuXdprHYQU3Hz7+thkf5ZcfmWBznoXS0gUGtuxmH9pllLtyZXIjbde4uN3P90V50s7yR5gar7OpZfnWb67Rm+rz8uv3Dx1k5VxLFYq3GttUS8cXwLb1j33d7VmupbFS7U6762u7CT8MEkYJimpNkYgJdel5nl045gwTXm1Mc3GYGA6TISg5Jo+fCkEUZowt0/HXsgKjYLP40FMZYxOjhY2mfYRxGhcwjQFNeSzDvSSxyy3WySVMgUrpRcnlD0PpUP6seBx2ENpjWVJap4/ikMSZSnlksP6UNBPt7CkoOJ61HwfgUJnD4xxiE55YvaRoVOFltMI+zpClhkmCZvh0CTmbJa6SHGtxGzcHkLF92iHIb7jIEhIUsVK0qCdhcxZKbbUJJnCsSRxlrG4y/FN4+1M3G6TqIyKJznSHFwPwDq61LSN0j4QHql1L4RAy2mzabstDqdDkJUDyV5vG7Gkn4Cdg2oFHgAAIABJREFUwPbJSm8fz9nR8TxZmVZpH0R0RAlpdNy02tsppUOQ9aP3tfRw4uN1lkyS8PvNZvNuEASy2WxuAL8UBMH/iXG+Oop/DngJ+JkgCOYxn8o6QLPZ/CwIgmoQBNcxVws/iVHlPBW26+Dj8Aoet78RkEQJlm3tSCQ8TwrlAm/+4OtsPt4iHETUZmvUZip72xuLJs40TsfGKYTg2utXWLw5j3/H5pW3x6/ETpu5Uom7W5tkSh07WNWNI96YmTvwRXh9Zobvrq7QCUM2wyHDJEEKgRwNWamexpGSreGAJMvYGg7pJ8no9TRZV+NYFjOFInXfP6BjL2SJenGRx/2HKO2NLQWlzOFxj2EmudfaZK6ocahQ9T16jmMMR+KMR90WjWKZON2kky6CNBPFcZryMGpjSYvZQpFu1OaeLtEjxLZilIYsU3g2BLU16u7QJAW593M006Q94uG3+KR3hdWh2RC3pQRdYV54lOPH1AqX8Q75Wy27Lq1wSJykhMka3aTEp1EFTURflJBqia3IZ7FcwZHWnisrJQpkurhz8lNoLAEl2wLrsM1LDTp7YpJyLLaRaFDLR59E5LTZuN2eaiU2Q2Z7Xjs1vfzZOlgLiH1XUya2Njp51/Txn2hAywG5MNpgHrNRLGy0NTtm4zYC66VDn1WPThAXQbt/krbMh0EQ/BXg3SAIfjkIgr8FTPJJ/3fAXBAEvwv8U+DfBf71IAj+4uj+v445afwu8KvNZvPDk4f/dAghcH33XJL9Nq7vsnBjnutvXGNqrrbHMWsbKeWxcTqug+M+P80O33a4MdVgczhAHdGC2BoOmfILezZTt5kqFLk1M8t7KysMErOCLrouvuNQdF3Knkc/TnjY6eBYFlXfp+ab1XTZ9ah6Hkmm+Hhjg5liaWxC9/2XuVQStMP+2DgVJaLMYan1mCkvplK8jms9Wfm6lkvBv0bVjflw4z5au7juFI5tYVsS17Ypj4a+vvXoHpbo4/kv0ygWqXo+dd9nulSkai9xb+seW1HhkGEjQaILfLw1RMV/zLQvaRTMc1T9BsL5ClpFLHfXDtX2dyyLkuOy3FtHEDOUb1J0q5RcF8dZZLrgk2YJn2xuMF86KLKWMIvFEI2mH8XMFS2EPXO4JIDugDV3aClqHMKaB52MlTx+8qAiyIZ5fhVijF6eJFatNaT3TPlEFsZuZgshTK++cNHJH6P1yeTLhbU4ivMQi0xrmh3VUjCTwKJwsHNnN7oz8jA4f4mFSTLFvwFMAf8LZhU+DfwLx/3SaFjrXz3i/t8BvjFZmKeL1pokTrFseSEHmy46V6s1tIa7W5tYlkXFdbGkRGtNP0mIkpRGocDrs7NjrwIE4FqSLy0scLe1ycagj2/bSCHJlCJMU5Is5eVGgxv1KaQQtMOhSVQaQNMoFAlmZnjQaXGpUqHg7C15CFlnrvYn0Pwhj/pthCxQchykMHEOU8WDls+8lzBTXhzpu+8lVDVi7XK1LPi4W6ZeyPAsduQe4iwjSnpcKik2k2tMUd+zgpJ6QFFsYnmzfNZuUfZcXOvgV+5Bu02ibaqOQ8IaMU9WtUPxVRxrHZ/32OgpLlXnYd9VAiojTtrMFxKWhq9xL76JY6VIIQi1RagWeal2n1ZUYLnXpeS5e/Y2FFV6WQOyZeYKNXPFZB2yD6G6pp/ePtzxbRxCltF2AOkHaDk1VlhMCIG2rpoSSbYCzlt7SyuqB+lDM4tgv3LkCUeIAlpH6GwFYV879HHj43zV2DmOGb4SooCWl0x5Ds8Mw1mvHFG775q+/hPEcJZMkvAXgf8AeA3zdXuf7YmMzyFxGPPxu3fptwcIS3LjzatML+ZesCdBCMFL9TozxSIr/R6POl2y0YqnUShwa2aGqucf2snTiSKiLOPthUVemZ7hfmuLx70OqdJ4jsNr0zN04pCK69GPYq7Xp8xJWikEULBtnNG+QCvLWOn1uD51sPwgnUXmp36QauEjWoNVNoZtlLYQKAoONAo+pdI/T0SCwzpo09ZoiwGW3qIdDdlMX8F3qjQKD5ktRkSpINHgW5rFksWjrkcsX6UTFejGMTX/yZ6BrdfQ2DvJdXM4ZKG8t9wYpSlbIyvGDBuHFWJ9aVTnBy08OvpHqVgeIn6XMH6E71QwvRMhZG3CpG1KLP4PUPH+JJdCM8eQaY1n2cxUXmXam+GmustSJ6QThkixvcUrUGjK7ixXSpqSNTT6ObsULk2ZZGBeT9QQzu2nWq1KexGFgOxDtBJGw3/X3oRpSR2AvALu94PaMoJ12y0d2WMzF2Bfm0ymQJSNJpJ1eXKfXEDal0ZxfoTWxnBmjym8rIIqGalqK3hS29+F6dnvmPfovHEhVvcwWcL/Nczq/lcwh/4bwK9jJJM/d9x97z7DfkhttkqWZnzync8oVgrPfeP2i0DJdbnpmlX4ttLlJANZq/0ejmUjhNHQuT03z63ZuZ3nGKYJd9ZiI2MgBd0oYq48/pK57Hk87HbGJnwAaTUolb6fYqHLYraJ1glC2KwMJY+ThHRU4030VSzdQhISphERN/m4G2HZPkpLOlmdMoKFqkaQobHppAVW0wFVr4BrJWwM+nsTPutkmA3IouOw2usfSPjdKEIw6nPHAp0hGaJ48jgtC3T0nyGWryPSD7nsrIAeIkiBAuvpddbka7hiEVvazJXMXstuFEViUaVSuMv1SopnW2RaIxC4lo3vFMG6bfrg9ZaRNdieYBYaRANhByCqz6AOCtJeQFtTpsMme2BWwIjRa5SNGNmO/MJVtA7NRjcCHX0LrKmJX18IeyRtPDy65DI2zkW01RjFubQvzirC/zMj9ecVUGvm30KYx4w6hrBfQ1gze08W58wkCT9sNpv/9a6fvzVq1fxc0tnoUZ02H75lW6AF0TDOE/4zIITAOoEfbJxlB5yPdj9HpvTOMKsUFok6pJ4K2FKSqQSVbYBOAGXKBbKy55JfyIqp7Y5IBi0s8aTfWguXVJitqX6Wkco5Yv2Q8igQS1iEqkiyy1Eq0aFZlY7i2C8BYRLyqKVTSNIxejmpVsg9gz4aqfujqw0HLVzTESJsMrHIFle4UqkAGVvLHzJbuc3msI2Weueq4DCUqBCK14hkgZmSROt0JMfsmJX7TiKtmdIKKWZpbZ1q0hLCQ9hX0Nal0WsYTf1xq2AhfBCjzhxx9PDWWHY0+Z81zmT0ZE/iFADWtDkpqd6u4+k+84nxrJgk4X8rCIK/gTExl8APAh9sSy+chhzC86RULRD2IwplH6UUWmsc7+KcgV8EbEuiYpMchR5i6w0sWkgyFDaJKCNHqySFwtlfsx4hdITQq9St+5BsK0iK0YabHrU7Xhnb/21JuVOGOgxLWDsNI0rrAz39u0tWmda4lsbSWwg9UvYkwiQzy7TfjkkAUkizqawTHFYo8D4FvovGBQRKlxno28S8hNK2uTIaXZUoXUbIErbsEWWTVVmVVthWAWFVDgzD7MassM92f8skxElLHSMl06OmWfdhfBC66PS+OXFimR57UTtRicfEOb6EpHWMzrZMZw8RGtPOKiz7xFcVz4NJEv7XR///c/tu/2+YQGLhonHjrWs0v/UJ7bUOGs211y/ntoHPmdliieXuBp79GTZbGDONAgobQUbFfsyiu0XGLB01h2dbPOq0GaYpUkDF9Zkrgq8/YJAMqfszB1retuvOOv422r6OsK7vSRRV1zuyywhgulhgdTCg4rpkWuFaksfdLqlWuNKi5ntYUqDVEJ9H3CyE+NokajRYbOCwTKyv0k185gs2Or1varsoED4Vq47Ua1T4AMkAjUXCzR3pYcGQMn8A/BG95Eu4xa/w3ZVlenHM/c11stUVPGmxFieHDrLtPy5V/+jBpCTL2BgOaIUhWps9k9lSidKEHgRnwU6fvu4xzsR9N0amYc3U/HXHtIIKB1Do7BEIa2S0cvmpV+FaKyOjnC2ZNlJZxKRTYxijs4doWUXYwYWyZZzE8epHAIIgcJrN5iEarp8ftrXsw0GE7VgXylTkRaHuSRrWRyilyGSd3WJkJtF5lH2Hlc4DZLLKb9+9TJgBCKQGKUNu1x5yvT6PtMrcLI3rmRYgSmhdgPSeMfzYpVdf8TxqnscgSXZ0fvbTKBRZ7nZphyEPWm3ubW2RjDxxlYaCbTHrQ72whGdpPO8a2W47Qu3i0cThLhU9YNqZB1U07YYApPj6e1zz3mGQzuLYVTLKe3TmNQUSCoRJHzv5v1nrKLBvUXAcXMuIx63EMfdaW3i2faQ/QDeKmC4UD32/WmuWOm3ubhmpAte2EcD6MOOz1hbTpRKvNaYP9Tg4a4R9GZ18Bzg84RuJ5Qemto4E+2Wj9rnnMelI0rhv6uwnTPpaK3T64UjYberAbMW2AJzRwn+aeYCz49h3GgTBD4+MzL87+vk/D4Lgx848sjPEsi1K1WKe7M8B00v9AS/VK/SSAvEh9XnfsvmsC+1wnaulNebLFebLZWYrZW5PbRFngt+7v4JEHJrAYHQ5LhuQfnZAP/2VxjTDJCEZI00NxgC95Lr87r27dOOIqu+bGEpl5stlijbo5D3ubm1h21PIfTV0RYGUMmm8ypzfxbXUyMTEMv9pIL1L2a5j6w5Z1kdxcDApTFLutQa4ziLX/G9TtY1iqTWaOp4rl7lcrfHu8iN60fi+834co7Xm5cb44R+tNZ9sbfLJ1iY136dRLFJ2XUquS90vMF0q0Q5DvrPymPiQ43XmiBqIymgD9RCyVZPsRcmU+OTB9yuEba4Is2V09tmJw9DZEmTLow3Zw0tDQpZG8wDfO9zv4DkzyantFzBlm23pg/8K+Pkziyjni41ug25R8Wd4bXqaNFO0wyGDxMgoDOKYdjhkpd+j4vo0SpfxxBZp0iVOM3TWRWVtUgpcqdX4eHOdfnR0/dpspHlG63wXVc/nrbl5enFEazjco6ufKcXWYMDdrU0WK2Xqvjk5KaVBazKl8MUmVV9ScGsMk4xWOKQbR4RpyjBJ6EQRncihUbCpeA3INozi4jZqHYhx7CKNgo9A0I0VYZISZxlhmtKLI1YHPebKZWqFOhobj4MzipeqVW7Wp/h0a4uNwYBeHDMYxbDR7+NIyVcWLx16cmyFIffbLaaPsKas+T5RmnF3a/PI431WCCEQzm2MSmb7gPeA0Sp6bNok9QCsm0e3b8qG6RQ6gU698Ra+D0foAu2NuQA6Qj+FOc5ZMMm1WdJsNjeCINAAzWZzNQiC5+O2kfOFQ6ePYKRDX/E83pqfpxtHbIVDkkzjWpK657Hc6zJfLlNwbGOmbYesR1Uadpe6V8G2a1hSsNbrc6+1xe35YzwNRNnUVvXeJDBdLPJ9l6+w3O3xsNshU5p2HNOLIyqeiy0tvrJ4hVSZE1M7jFBa40jNjWoPx14k05JeEvNqo0E/TghHmj9V16FmrWLJN0FvQrpi6spyHtCQfATaNJk77lUWnISa9ugkNkmWYUmBbxfQWu9oAqXUKfAJQ/XlA2/xcrWGb9u8Nt2gE8UkSlGwbObKJSr7nMn2s9RpH3AFG0fV91nu97henzqX0o4QPjhfQaefQLaK3t5QFQLSNSNBbM2BffNY2WSjU8+JdOp1tgVaHSzjHIUsgVpC64WJN5zPikk+sbtBEPwCMBMEwU9hBNQ+V505OReFzAyr7FodCSGMjMAukbPNwYBOHLMw6r0XVpnLxQG+V2XauktGzZiEYFadH21tHJvwhRBmGl73DwzK+LbD9akprtXrJFlGrdfjzSvXeOfRQzzHQQpj1jJbMuUcAFv0qFgWsXaQQBJmdKOYK7UnJRmtepAoY+TBJRCNkQ5LAVQfUGC/NLLKE6AlBQYUvCdTrlvD4T7ZDRtIR5vdB9+jJSQSeah/wDiiNGVrOGBqAkE8KQRaaVrhkPny4ZpVZ4kQLsK5hbZvoLNVM4FrhOyNe5hsTJ5YZcmYzEyqU69WRxu0J4nXMwNkesiObeQ5MUnC/2sYiYTfA34A+EfA/3qWQeV8UVFmduWYL2OUpXsaAjUSidFgkWTsvte1LdrhEK0U4hgxN9M9c/jFqRQCz7ZxR36wnSjGtcY/p0TtEfSWQjJI99dpM/b0PgoPRBnh3ERnLUjvgLV7/kMCe7VmUqU4aDmOcZEaF5eURv3zBKRKjXrcJ0uSlmWRZOd/kS+Ev0eyQMURMHnbpsGCE+ntxDyVM6wAOP9jNknkl4EPm83mvwP8ESbpv3KmUeV8QZnsi3KwhqzRO0l+e5LRoJRGCgXqMTp+Hx1/Fx1/bFbXYzk6GWitd2rDjiVMzX7c49ieqnzye86Bbo+D7wMys3msB0Cy7wSkzVRp1jL1/mwLixB1wCpCmH7vsXHpA0NtxyF3NIomQx0YFrsoPE2JaXRlcKLXOJlv8fbLHPe39zyY5Aj9MvDvB0HwA8C/BfwcT/xocz7HxFnG1nDAMEkRwtTU635hYjerk2OBKKJ1tKeOninFIEl2hpsqrodAkqYK25ZYRMTa1GMTXcQSMRk+kGJlH/G1qVUIiySjk4IlMmQi0NYCOLcR1rYwmh6re5IqxeZgwFKnTT+Oub+xTv/RA0q2yyAZv6GX6e3n0ShlEu30PjmDnfZLrczlfPbQ9GxjmcSutkAlYDUAe1RaKPJkFakpyhiPGIt5MirmPaBRlGGMg1SWKar+ybrPfNvGsy3iLDvSZ3gbpfWRFpXnhqyZvvhJdHa20UNjIznxa1TN53gCk5XtKeETxXVGTJLw05GByS8Cf7fZbP5+EAS5xOTnmFQpPtva4mGvA9pMvmptjC9cy+JmvcFi5Yzqs/IqpE2wPNJMsdLvsdo3piLbl+KWEEwXfdYHA2bLZWwR01VGb3yg56mJT1BYzMh3Sa1VPPcKn3SgnxgdeseymCsWqHkbuOq30PafAmcaZPnA0E47DHlvdYVUK0qOy1SxSMt1EQg6ScTWMKTg9Jjfp+WjcIn0FI7oshZLFssVKvsGk4Rw0GIKsrumZq8TcF4xdWMA9TJk90wCyXpg1cCZNS2bI3yniC02keoxQoZoXBLmyUSd/Qk/SlNKnnviZCyE4FqtzocbG2PlrHczTBJqnndkv/95Iaw5dHr3RNO46ARhLRz/uO3XkHPo9N4JX6MH1uWxCqHPm0muZewgCH4WI4n8z4Ig+DpwPrs1Oc9MphTvr67ysNuh7hd29Ntrvs9MsUTBdrizvsa91lH+o0+PGW23iNMhH26uG1N016XmF3Y2b33HwbUceklMe9gi1R6JNgk31hU0khn5beJkjaGa4kF7yGY4NAOPAuI04dOtLZqbMYOkAOnvQ7oE8uqeL2knivj28mM826ZRKO7pOvFG06Xfd/kKH22ss9brH3gv/WyWQdTFwih/jk0AAmPWASah7960sy6ZNs2sD9Ks3A/+vmCqWGGYulhs4vKIIbf3DKuB+Vw7UcjN+gk2LHcxWyxRdl06h/Txg5nAHSQxL0+dv5HHOIQogJw107UToFV3ZEoz+VCUkNua/cea/pnX0Jk5qchjusieE5Mk/H8NGAB/qdlshsBN4N8+06hyzoz77RZb4ZBGsTi2dONYFtPFIp+2NmmFwzHP8GwI4YD9Og/bD0iycCSjvPfP0BKShUqFW9NVdDbk/a1pNgchgyRhEGc87imkuksqpgCBb9vG7MO2sC0Lz3Eoez5hmvJJa0CcaUjv75Ff0FrzwdoqBcc5sr3wpakpfuT6DR522yy1TI97OwxZ6/f5rJMy5AY/fG2Kqnfwolfr2JRt7GtmdS+q7N3pLYIugOgBNXPJP2bvoeS6TBeLxHGPQeLt0e5XWtOJzBzB7dm5Y1foh+FYFm/NzeNbFuuDPsPkyaZwMir99eKYt+YW9iiCXjSE8yoI5+jhLEYdVAiEEzzFa7wGSONxe9Rr6FFXmv3asS2iz4tJpBWWgP9y18+/eqYR5ZwZqVI86HSO/cJKIfBth6V2+4B94GkwyCo8DF/isv8QdMsYaO+63BU6RjJgruSSyW/wdu0KzfU1NsMBFpI3phWXC7dZaq+TSg9rTCeNEEaSOIrbtONpZm3Quo0QxvugE0UM0oTp4vFfxJemGniOw7RfYHXQN6UTxyWYmeZSpYZQ60Y7XaWm5XKk20L6wCRwawEKt0F1jcaLHq3m1QCcBdCzkD3CtK22QFaebCRqDbpP1e7hVgLWo0v0olUS5unEkWmPLJW5XKk+c5nFs22+vLDI1mgIa3MwAMzJ4Ga9wWypdG6yCpMihAvO2+jkDjrbGClXGpevvbr+FYRzi6cxPBfCA/dtdPKeeQ1Z3KPMalb1XdAZ2MGhpvbnwcX+9HJOlVYYkml9rBctQMlx2Bwa4/DT/pKv9XsgpxmIGSy9ictjhB71UiPQeETcIBUNtIwpOg5//jWzElMqhsHvs9y/wnqcMl8YIhmgsEedPKaLxyIBoRmKKve6daaLAhk/AN8k/OV+d6z71GGUXY+i4/ITr4zp15ZzaMsYcOvsEWbT1ZiU476N2JZUllW0XjD3a22SvKyZk0S2AGoJ0keQWaMNPj0S5poC9wfw7StcKWXMpwmxdYlSp8tbV65NtNE6KZaUzBSLzBSLxl9Ya6wTtGxeBITwwHkbdMd8Htk6evvKSk6PdP1rz/SehCiA81W02oLsvkn8251bQoJ1BSHnLpRwGuQJ/4UiztKJO3DE6EueKHWIMOzTM0gSXEuONOgXSfU8gnjkvWQZaeDtDVyZMtzT3x6D0gyzDKXL9FQDiwhXdJEiHpkjSUI9RaJLJKPW+wwbyZNySZhkOCdIlI6UDLPD9VCEsMCa3lM2UqrHfjNso71SMCMBGSNdHQH2LDBrzL5FHaSHMdUu7xkk0lrgyCGe51G0nVNN9vvZNpb/PGLE82oIWUPbmiea+6f3foSwTAeYNYNS3VHZzgKKSHkxJdfzhP8CIUcpdVLMevsM4hCmK2gHIdH4YyPTmn3mKmYVZQk5erwko8BQF8bueRpTiu2fnvy5SynQ2UmPxUmPhsXRR3HM7bJgLPzkYX0RJ+0bz9lxEztlTOmmbSSvdXskkwEIC2VdQZzQ6P15kP/lvEAUXfeA4NRhpEphC4l/SuWcTCmy0Ws3Cj7RganUQ+LI1B7ZBSiA9Km7mDL4rsEoPaqA7CbKUiquj0MK9pPV95TnE04YA0CYJDQ8gc7W0NkKOts4XnRLVkCHR9xf2jvluR38Uf6n2tSfc84XrXro+Fvo+D0gQchphNUYdaGVTJkn+gNU+tnE37nnQb7Cf4GouKZHO0zTYxN5Nwp5qTY1Ub3/MDKl2ByagaZuFLO0scbw0QMulSsoLciUOvL54yzDsy3Krsv6YMAgiZEI6vI2Jftb1Ao+nSjEtWz6cUyUZmihsRCUPRfHslAKLpd9s7SRV3eee7ZU4pOtjcn6qdUAn4+ZcyU6sUe6N2alreUiwr423p7PvoKOv4sZphqDnDI1+21bLUKQtWMMuiOE/drR8eacKVp10cm3QfgI2ThwvxA2iCnjuJV+ZvT37ZcvxD5InvBfIIQQ3KxP8Z2VZWwpDx3BHyQJlpQ74mVPQ5xlvLe6QjsKKbsejWKRtushEHy8ucEwjYl6CQvlytikn2QZnShksVTh/3u4ZDRlpCmRaFXkhtvjSmmOb3W7rCQ9So6HaxvVGaUVG4MhcZbx5tw8dbcD9teR8klS9mybxUqV5V73SNEwqfuk8R8xVyjiOnv7+LVWoJbRyRY4XzqYqEUdhI/Ww7GX9kJ4aFk3gzkUzOrdeunQWLQOMcqQBzXzc54PWsfo5D0QhWM7fISQxqUre4AWJYS9+JyiPJy8pPOC0SgWuT07RzsMaYfhHg34JMvYGgzIlOLt+cWn7s5RWvP+2iqDJGGmWNpzNeHZNo1iibpvukA2hwNawyFJlqG03omhG0fMFYs87nUpOi7TxRJ136fuF5gqzrOhfoC1/sfMFW0afpF0NBTUT2L6SYprWVyt1SjIdVJmwX37QJw3pxrUvQIbo/e8n0zFJOF3KLhlLteuHFihCSERsg46RScfHPh9ISTCecPooetDZhrsKyOJnRWQlw6t3Wsdgh4inNsX0hz7RUGPPA0mbecUQowkH+6bBcI5k6/wX0Dmy2XKrsvjbpdHPTOVqEeSBDcbDWaLz9Zv3YlCWsOQ6dLhK+eK5xFlKa9MTZEpeNzrkiqFZ1kmEfs+7y4/pl4ojL0SSeWrfNhZ4e36B1yu+nSiGu1Yo7XCsy0aXoRv9WnFczxKfogb8uAX1JaSN+bmuN9u8aDT2WlZ7SYxG4MBvtjkWslhrnLpyNKTkBVT01e9A1ObQpbB+bJxPVIbRoNF+JgN29T06dtzoKcA98DVgNaReYxwEc7bR2zm5pw1xiv3vtmbOQFCOGjdNRu7YjLjlLMiT/gvKCXX5ZXpaa5PTZGqDIHAsaxTacN72OniOcf/aZVdj7XBgK8uXub61N4vwmqvhzpC+bEVDonFq3y7vchXvT5V70Pq3vYGqCbmKi1uEblzbPRjrk2N3y+wpOTGVIOrtTqtMGSQxKSFIm/MzTElN5FyYbIVtXDQ2crYMX0hy+B+fdTR8WA0lKNM66X9MsKaxQiqmfu12nzS3COKYL9uNgTFxWz1e2HQfVAhwnqaqVnXGK1M6JR1VuQJ/wXnqFr+09KNIvwJEr5nWYfKN3TjCPsIV6FBko4UHsu01XVS+SUkIUZJ0h31sZtmvEzFJCo7cpVujwaOoEi/WGK2WEKFQxjjMTsW4ZmEcNjdQoKYQrhHfOFFA+E2Rv6nCpPxnQux2ZcDkPHUjcripLr7Z0Oe8HNOHdPE8mytaCbJmecQOsZiNNiCGdgSIttRKAAB0h5JBh/yfE89UTDpNMJk79fU4sPR420QpQNXEM9jJW9OKilmG8/J9wUm5ZlOvud/4s4T/gVE66GxbsuWR6sCy0xb2pdAVC/8iq9RMJoz25OsWmvSLDMbs0rtWPb1k4SpQ7R66r7Pg/ZjPPUQm3UE+onph8647GV8PCyQibkjW0yXMj2JAAAexUlEQVSTkcb7SaZqd5B1s2oXE1zC6xCs8baCRsOlhU6W6EUrDNMErcC1JSW3guPeQFizZ57oTRxt4yus19n200XYaOsKwpo/piX0Rcc5OOgxKToxKpvnTJ7wLxBaa3T2GaT3zTSlKCNkyezu6zY6WTEtec7tY7+YeqSiuNbvkyiFa0lmS2Uq7vFG1c/KQrnCg26HOEnYjEJWej0ypVjudhiurjBXLDFdLBImCa9PT++oTybKbLjOFEvUnJAZ6wOUcshk9cB0acGN8PmUS6UYW8wbOYYxdKOIlxuNp9qbEPZldPJd4OiEbxJphrAOGmlordDpp2z1P+JRNyVSDlI6pn1UKYRoMVt4h/nyLI7/9plNZmqdotMPjScrBVNekttyDclID+Ye2r6FtGaOfrIXFCGLaFk5tM32SHSCsM5fIjlP+BcErTU6/QSyByPtlCcJToySP5RHQx/fAefLY4d9wJhUvL+2SjeKcGwbWwpSpVjqtKl6Pm/Mzp2p6mHF82j4RX5v6TPKjkPR9bClpOc4FGyH5X6PjzY3eGt+no83N+knMY5l4kyGigfth1zyP2GhusDddh9HZhScJ8dDacUg0cxUr+LSx9FNYm7tUdwEMzxWcl3mS085TyBqIKpo1UHI6uGPU5tgLY5NAjq9y0r7Dks9h4pXxt+3L6G0Zm0Y0YvXuTn1bdzC1w79XJ8WrZVpG1Vbu9y/niCEA6JuEn/yHoq3kNbF1Lw/b4R1zfThW5MnfK1DkOUT6e6fFWea8IMg+DvAD41e5283m83f2HXfu0B718O/2Ww2H55lPBca3TL2bHLmyBW4kBW0aqPTTxHO6wfuj9KUby8/BsFByz2MLPB3Vh7z5YVLZya8FaUp3ThioVShE0eESYJn26RKMUiNleF0ocD3Vte4NTu7V6LYAV99yiCRRCrilUaD5V6P9nBoVqSjK+rZUonFcoXN4YCVzj36PMJxLiGEIM5SojTdObk9VTmH0YnWub1LBre6p+xiWiY7YM0j7JcP/L5WPTqDj1nqWdQOsY6UQlDxfPqx4GFnjevOEsI5+FzPgs7WQK2PTfa7EcJByxqkH6Dl918Ih6YLh6yDrKBVd6IWWaOJ3zXqnReAM/tEgyD4EeDNZrP5jSAIpoF3gd/Y/Zhms/nDZ/X6nzd0ugSiOFm5RVRBraL1jQOlnYfdDqlS1AvjVyBVz2NrMGCl1+Nq7WwmNtcGfZRSvDYzwzBJ2BgO6MWmQ2G6UGC6WGSjP2ArCvcpYYLUAyw6FNwp2sMhYZry2vQMYZoSZxkCKNgO9kgDf65cpu7foDXs8yg2cg5TXoGFmTI1z3/m8pXRV39rtKeyZAzIhRzJFhfBvj2qvx/c9NTZCquDkKJTObakVHI9NsKUheg+BfvaqdXzTe/40sS940I4aJWisy2EPX5P4kVGCAucN9DJd9CqfeSemtaJ8TewX0da51+/h7Nd4f8O8Aejf28BpSAIrGazmY1uyydIRmgdGmekCTd1hBBoJdDZhtnIHZEqxcNO51gjjLLncb/T4nK1euryt1prltptyqMYCo7DFcecWLxun6u1OmmWsTboM1MosTbos7hLXsHSm2wPgJc8l+Vuj9nRtO5hm7OuXWS2OGSuXjuTy2YhHFPPtxZHBhojI3JROOLLnhFG92nHLnV/sq+ZJW1a4YBCoQ2nVUfXfVD9PbLNxyKLoB4CecIfh9Hb/zI6+QjUGlrYo439kTrqdieW8MD50oUqj51Zwh8l9u3G5L8K/OauZA8wHQTBrwDXgd8Gfq7ZbF4cWbnniY7hpCYTwhklnyeEaWoMK47pq3csi24UEWcpvn26nSGJUsQqo2wdftKJs8xIwFsSUvM72zELIjQmJltaDFQy2nQ+riwjgcnVL5+GJ3spk5CSqhQpJi8neZbFIM3QOjzFBr5tM5aT4I61Wsx5ghAuwn0DrQZotQpqBVQ68jcojawW6xeu3fXMi3RBEPwF4KeBH9t3138E/AowBP4x8JeAX9//+3fu3DnrEA8lDMPn8vpS9PCtJTLdPv7B27/DgEx3iFWyE+cgTVlqbdJ2j2+t68QRtV4f75Tr+IlSLG2uj40hjiLu3r1LmKUsdzt0HZdeklDoD3biKDsPcWWPTBsphF6SUB4MccbYGO7Gok2oXJQ+YnN1Qk7jcxfEKB6x3BX0nMlOqnGmKFh9dOtj0glMsieJ0xJtvBP+bYFCiiHD9HT+9p/X9+hZebY49//drYz+O32eJc6z3rT9ceBngZ9oNpt7/uKazeZ/u+tx/wT4EmMS/q1bt84yxCO5c+fOc3l9rUN0NNjjbHTs72RbYN9E2pd34kyVYrB0n4rnHbnKT7KMYZrwpSvXzqSk03uwhG/bBzZL7969y40bN0izjOHKMkXHpZIlvDq3sBOvoxxcHpGJGqnKqKcZr87PH3tcdLaBcN84lZLOaXzuWmcM+5v0N0Lq/mSj+N04YqEQszj15rEbrJPGaXTboxOVdIzQm410T+dv/3l9j56VL1Kc77zzztjbz+x6IwiCGvCLwE82m83NfffNBEHwm0EQbC99/jTw3lnFctERwjfa6PtKNIMkoRNF9KIYtUvNUWsNQh9ICraUXK5W6UZHj3D3ooir1dqZ2NcJIbhWrdEbxaC1ZmPQ56P1de53u6z2ekghmC2WWB/2mS2W9pycMtHAyApAP4pZqJSPT/Y6MmYikwxIPSeEsPC9q9TcmChNJ/qdTKXU/aJRVzy1QEogS+YYTYoagLxyejHkXBjOcoX/U8AM8GtBEGzf9lvAd5vN5j8MguC3gf83CIII08FzYHX/IiHsq+j422hdpBtFPOx2GCTpjteGLQXz5bLpKdcdkHNjh68uV6qs9Hr04piye7CfuzPSuVkon92e+WypxL1Oi6XWFh+sb7AZ9pFCstVpsfzZJ1Q9n1cb0wgExX17CEoUyXSVMG5RcMs0/AlMoFUX7FsXbgJZWAvMFT/hw60QxyodeYLtxxENL8H3XjvViVshBFpehfQOHLGvso3WCQgbYZ2vyFfO2XCWm7a/BPzSEff/IuYKIAeMWYZ1lU7/Iz5qQdH1qPlPJH0zrXjY7hBGW7w0NY2wb459Gs+2+fLCIu+vrbI56GNbTwavkizb6U0/S/Nrz7aZKxb5Jx98gLQE04UitmXBYECjWGJjMOT/WbrPv3z7TaQQbAz6O4NXqVK0shkW/TZXa5Wd9svD0KpjXKIuUCfENkKWqRZf4Upyhwe9IRXPOyAIp7Smn0T4MuJKdQZhXz3k2Z4hDmsWrdZMq+URidy0EbbBeSvvwf+Ckn+qFwQhBDEv8WF7mSlvDUukZLpkpke1wmFIw4/YCB2K8cvMe4dPYxYch68uXtqRVoiyFM+ymSuXqLjema+EM6X4w4cPuTk9DWg2h0OiNGOQppSylKu1GraQfG9tlW++9TbDNGG9PyDKjPXibGmBsnUJsvfRKh0rMKZ1aoaeZBnhvHFhE5Swb7BQ03j2Rzzq9ugrBymtXdIKKTO+YqGyLa1wulO2sD1A9jo6/dB0lFDYM/OhdTpy3dLgvHmh2ghzTpeL+S15QdkIh4RcJpJXsfU6DisInQKClDqpeAXcAve7IXPlo71YhRDUfH/PVcLz4nGvx8ZwwOVqDSEEjUKRTCkKUcTVxsxOaWOp3Wap0+KVxgz1AyJqHlp+FZ09ALWCVtq0vKGNlrxwwL6OsBYvtE68EBLsl2lUp5kqLNGLlkfiaQLHEpS9Oo57fTS8dfrJ/kkcNti3QF9Cpw9Bb6DVE/E0rGsIa25iJ6eczyd5wr9ArPcH+LaDFg6JuELClV0G1wbPhs3hYGTwfTE/vuVuF0taOyckKQTSsnCk3FPH9myLh90urzTGd6QIWULIAK1vgGqPJH0lQrogambq8XOAEMKIlXlTVNxXqeiIo+SRzzaOOsKto3WM0XfP5ZFfJC5mxnhBUYxZtY9ZxQt9sTYn96PIJuoAEgiUOn7WTggXrNkLoCb+7AhRgDNSxDxZHGd3NZFzcclP6xeImucRpkdPiyZZhmWJpxYEex40CiXiLDv2cUmWMl2YoAsnJyfnVMhX+BeI+XKFe+0WSutDV8jdKOTlqWmkEGRKEaYpgzQlybILcxK4VqtTch36cYwANsMhwyRhvdcla7doFIo4QmIJySuNiyEqddZorenGMY97HTYGQ5TWFB2bK5UajWLx1G0mc3LGkSf8C0TRcbhWq3Ov3WLKLxyYlu2EIUXHpVEscq/V4kGnbXTuW5v0H9zncqXGlWr13Gv7rmXx9cUr/IP3/hjfsih6Lp5l40pJnGV8urlBP0n4F4PbFMfMCnzRyJTio80NHve6uJZNyXFGMs4Z76+v4VkWb80vjJ2byMk5TfKEf8G4UZ/ClpLPWmalb0tJppXRkPeL3Jya4oP1NTphSK1QwJaStutR9XwedTusD/q8vbBw6qJoJyFTilRl3Jqd4dOtLdphhG/bDNIUEYZIIXmtMYOQXKgrk7NAa81Hmxss93pMF/bKX28rgA6ShO8sP+ari5coTKi7k5PzNOQJfx9KKVbvrfH47iqfffoZRVHh8ssL2M7zOVRCCK7V6iyUK7SGQwZJgmNb1D2fkuvy0cY6vTg6YG5iSUm9UKATRTTXN3h7YeG5xDuOjcGAdhzxlcXLvD4zx4NOm9V+D9VzubWwwNVqnZLrsjnos9LvcaV6Nrr8F4FuHLHc69IoHO51UHQc2lnGUqfNa9O5vWDO2ZEn/H08/nSFBx8+otIo41d91u6tE/VDXvvaK881DteymCvvFQKLs4xHve6YnvUnVD2P9UH/UGmF58FSp03ZfaKH/+r0DK9Oz3A309yYeeL7WvF87rfbXKqcvi7/ReFR15Rxjht2q3gej3tdXqrVz70kl/PFJd8p2kWWZTy+u0ptport2Egpqc5UaK11GPaG5x0e/TgGODY5CiHoRuHzCOkAcZbRi+NDzUp241jWjh3hF5X1wYDiBGUaKYx9434HsJyc0yRP+LtQmUKlCnlAv0WQpWrs7zxPlJ7MH8YSgmzCx542Rslz8scLISZ+X59H9BEdV+Mff4bB5Lzw5Al/F47rUGmUGHSfrOaTOMWyLArl8x85dy1rooSQKkXhnDZtHcsyJxx1/AlSj97MRdm01TpGqx6CwWgS9eSEaUI3iujFMalSFB1nopkEMIN3eXtmzlmSFwv3cf2NqzS/9QmttQ69jT5hLeTlL1/Hss8/KVU8j4rrMkySQ7s5kizDlRb1c9DQAVOauFSp8qjbPVbHpxfHzBVLZ6rcOQla9dDZI8gek2QaRy6hox7aWkBYlxDHGIBrrdkKhzzodNgcDpEjSWspBJ5l0YpCFitHO3FFaUrZdfPWzJwzJU/4+yiUC7z1Q7fobfWhnPKl77uN416cVrlXGtO8u/wIS8oDiTJTilY45Pbs3LG+tmfJQrnCUrt9ZMtlphRRmnL5nDt0VLqMTj5gK8x43FfESrPc1vTXYxZLD5jyHiHdALnLLH43Wms+2drkfrtFyXWZLj6ZHM6UYisc8unmFo60mCmNN2jRWtOJIt6am7twmv45Xyzy68cxWJZFbaZKZbp8oZI9QM33eWtugTBJ2BgM6MURgzRlczCgE0Xcmpk7U3OTSSg6Drdn5+hE4c5G824GScLmcMDrM7NUveNNOc4KlW2g4vf5pJ3xWTvFsmyqnk/ZcbAtm3udlI/bGVn8ASrbGPsc99ttljptZoolis7e1bklJTPFErfnZnl/fZXVXm+njLVNlKZsDAZcr9eZLT27PWNOzlHkK/zPIdPFIt9/5SpbwwFb4ZAtx+G16Rmmi8VzL49sM1sq8VX7Ep+1WmwM+ggEnThiY9Cn6vl8ZeESU4XzExHTWkN2l0d9TTfOqO2LxZEWNb9AJwp53JdcsT9G7/McjrOMz9pbTPmFI1fmU4Uir8/MEaUJW8PR/pAwMRQdhzdm55g9ZPWfk3Oa5An/c4otJbOlMrOlMtnqOouV813Vj6Pq+XxpfoFBkhCmCV67w1uXrlC6CHVq3SVNu6wNs52ZgXFUXI+1QcR8sY9rt40z2Yj1QR80E5XPZopFWuGQr8wvkmmN0hrHsqi4bl7GyXlu5Ak/58wpOg5Fx6Hmuhcj2QNadRkmpnvmqLZJIQQaGGQKR3UQ8knC3xgO8SeUQth+jUzrc72yyXmxyWv4OS8oGQoxWeO7YOQOtbe9MlMK64Sr8y/yzEHOxSdP+DkvKC620OgJpsS01thSAXtLP75lk0wwb/Dkecj77HPOlfyvL+eFRFh1Sq6NIwWpOnwwKtMKRwojaWxN7blvrlwimlAKwbSoyrzPPudcyRN+zguJED5Y81ytSLpRhNIHV+pKKzphxOWyhbCmjT3hLup+Ac+yJ9IC6oQhV6u1c52PyMnJ//pyXliEfYN6ocj1mkMviuhEIXGWkmSKThTSjWKuVR2miwWEc1AtVQrB7dk5elF0ZNJvhUOqvs+lY6Ztc3LOmrxLJ+eFRQgfnLeZKb1P1e3QijTtWGMJxaWSxZQncN0Kwr59YHW/Tc33+fLiIt9bXaUXx5RcF0dK09mTJCRZSsMv8vrsbF6/zzl38oSf80IjZBGcr+LZbeach8zpPl7HZb4yh7Avg6gjxNGJuu4X+P4rV9kcDHjY6zBIEgQwUyywWK7mvfY5F4Y84ee88AghQUwhXLMpO8yqSPfWiZ7DlpK5cvmAaU1OzkUiv8bMycnJeUHIE35OTk7OC0Ke8HNycnJeEPIa/ueUbhSx3OuyPhhwb3Md1la5VKlS9bx8gzAnJ2csecL/HHKv1eLT1iauZVF0XDzLphWFLPe6XKpUeXV65kQ+qjk5OS8GZ5rwgyD4O8APjV7nbzebzd/Ydd+PAv8FRpHqN5vN5t86y1i+KCz3unyytcl0sbiT1C0hKLseJcflUa+LY1ncnGqcc6Q5OTkXjTOr4QdB8CPAm81m8xvATwB/d99D/h7wLwF/CvhzQRDcPqtYvigorfl0a4u6749dwQshaBSKLHXaExtn5+TkvDic5abt7wB/efTvLaAUBIEFEATBTWCz2WwuNZtNBfxT4M+eYSxfCLpRdKRPLJhxf62hHYbPMbKcnJzPA2dW0mk2mxnQH/34VzFlm+1l5wKwtuvhy8DLZxXLF4VUKSZQ88WSYiJBr5ycnBeLM9+0DYLgLwA/DfzYrpv3py0BjHWGuHPnzhlFdjxhGJ7r6++nm8Tcb7Vo7zP+jqOIu3fv7vzcjiJEZZOuf7GclS7a8TyMPM7TJY/zdHmWOM960/bHgZ8FfqLZbLZ33fUQs8rf5jLweNxz3Lp1shH30+TOnTvn+vr7yZQiebBE0XX3CHHdvXuXGzduAMasY2s45OtXruLZF6sJ66Idz8PI4zxd8jhPl0nifOedd8befpabtjXgF4GfbDabm7vvazabnwHVIAiuB0FgAz8J/LOziuWLgiUlL9XrtIZD9CFWea1wyGKlcuGSfU5Ozvlzllnhp4AZ4NeCINi+7beA7zabzX8I/HXgH4xu/9Vms/nhGcbyheFypcogSXjY6VAYmYMDDJOEfhwzUyrlLZk5OTljOctN218CfumI+38H+MZZvf4XFSEErzammS2WeNBpszUc0o5jrkjJW/MLNAqFfOgqJydnLPl1/+cQIQRThQJThQJaa2YGQ24vXjrvsHJyci44uXja5xwhRK6dk5OTMxF5ws/Jycl5QcgTfk5OTs4LQp7wc3Jycl4Q8oSfk5OT84IgDhvguQi88847Fze4nJycnAvM1772tQPdHBc64efk5OTknB55SScnJyfnBSFP+Dk5OTkvCPmk7YggCArA94BfaDab/+Ou298Fdit9frPZbD58zuERBMHXgH8MfDy66bvNZvPf23X/hbCMnCDOC3E8d8XzTeBvACnwc81m8zd33XdRjulRMV6Y4xkEwU8Df2XXTX+i2WyWd93/TeBnAAX8981m8394ziFOEuNjoLnr/j+7y8fjuREEQRn4n4EG4AI/32w2/49d9z/VscwT/hP+Y2Bj3B3NZvOHn28oYykD/1uz2fyZQ+7/e8CPY6Snfy8Igl9vNpvvP7fonnBcnBfleBIEwTTwnwBfw8T988Bv7nrIuR/TCWK8MMez2Wz+feDvAwRB8KeBf2X7viAISsDfBL4PiIF3gyD4R/uVdM85RgE8uiDH898Ems1m8z8MguASRnjydXi2Y5knfCAIgteB2xirxf1UnnM4h3FoHLstI0c/b1tGnkfCP+54XZTjCfCjwP/VbDa7QBf4a9t3XKBjemiMIy7S8dzN3wS+uevn7wf+cNsXIwiC38X4Wf/v5xDbNvtjLAGH+4f+/+2dW6xdVRWGv0qVVOoNarGCtBGSn5tcGnkoHLBgIqAhFMRICqmF+FCSEpVIQtQCPoD60NAUtWJQ2oR7SjSEaym0tNALGEKhEf+mpBWP3IIhIGDAQ+PDmIvu7py9z3ZXzl5xje9lrT2v44xzzthzzTnXP8eX14Bjyv1nyueKvn2ZAT9YDCwEvjNK3gGSbgFmAGuIR+pBbG2aDAxJup/4w7zK9pqSV6cjI7vZCfXxJ8WGCZLuAD4PXG374ZJXF5/OoLONUC9/AiDpBOBvtl9uSR7Nn9PG1bAWOtg4GZgqaSXh69ttLx2EfbZvlzRf0nYi4H+jJbtvXzZ+0VbSPGCj7R0divyI0O6fDcwEzh0n09rZQqwvnEmcEbxC0sdKXs9HRo4D3eyE+vgTwk8HE6O8+cBN5bG+ymsvOwifdrMR6uXPiu8Cy9vS6uLPitFsfAdYRPj6a8D8siY17ki6EHjB9mHAacD1Ldl9+7LxAZ/45jxb0ibij2BRWawDwPYy22/a/jdwD7sfs8YV28/ZvrvcbyO+1Q8q2T0fGflhM4adtfFn4RVgg+0R288TUyafLXl18Wk3G+vmz4rZwIa2tLr4s2I2bTYWP/7O9ru23wJWMzh/ngQ8WOzaAhxUTgeEvfBl46d0bH+7upd0NbDT9uryeQqxUn52+Yf6CrByEHZKuhiYbHuppM8BBxK/eGzvlPRJSTOAYeLIyAs6NjYgO+vkz8IqYLmkXxC7ISZT5kpr5NOONtbQn5QFxrdsv9eWtRm4UdKnid1GJxG7TMadTjZK+hLwQ+JJah9giMH5czsxV3+XpOmEvSMlr29f5gh/FMrc2Tm2XyPmRTdKepyYN7trQGb9AThD0jpi2+MlwFxJ55T86sjI9Qz2yMiOdtbMn5TtiyuJHRD3AZcC8+rk02421s2fhWnAq9UHSVdImmX7X8AVxKh1NbHN8I0ObQzKxmeJnXqbgceAe20/MSAbbwBmSHoUuBVY8L/wZUorJEmSNIQc4SdJkjSEDPhJkiQNIQN+kiRJQ8iAnyRJ0hAy4CdJkjSEDPhJI5G0VtLRY6XtRfunSJpa7ncW9cNu5Y+V1NeWSkkzJd3ZT92kWWTAT5IPh4uBqf9F+WXAD/rpyPZTwEuSzuunftIcGv+mbVJvJB0C3Exo0k8ELiTefP0t8EXgo8CVth+RtBZ4EvgyMImQvn0RWEHo0exHiI/dM0afnwBuIkSrJgKX2n6mCFndAJwF7EsoWX6EeDFqUrl+j1C0nAMcJembpdmFkr5e2ju9qF9W/Q0Br9p+oXxeQrxl+T6wAJhS2h0h9HKuAc4Ajgcut/1HQmtlOQN+0zapNznCT+rOecBDtk8lgt40YC7wUkmbAyxpKf+Pkn4LMWLeH1hlu9I+/2kPfX4feMD2V4m3bReX9InAX2yfAuwg5JLnAX+2PURok0+w/RDwNHBRFcSBraXeX0u9Vk4D1sEHh64cYnsWIYxWSX8cR3zZLQB+DlxU7ucD2N4OfEHSx3v4+ZKGkgE/qTurCCmBxcC+tjcBJwJzyoh+JTCpRZFzdbluBAS8DpxQpAdWAAf00OeJxKvsa4FfA59qyVtfrsMl/QjiNXzorkdelfl7W3sQUrzD5X4m8DiA7XW2F5X0LbbfJUSyttl+mxBWa23rFfYU1UqSPcgpnaTW2N4q6VhCrvZnkn5PjKSvsX1ba1lJsHsQU0nGziVG+SeX65966PY9Yhpn4yh5Iy33E9hTmnZXlzbb67VTtfE+ow/ERjrcj9ZWkoxKjvCTWiPpfODoMk/9E2J+fjMxlYOkqZKubakyVK6ziNOppgA7bO8itOJbtfk70dr+kZIu61L2+WITwJkt6buIef5eeJFYY4BYgzi19H28pF/12AaEMunLY5ZKGksG/KTubAN+KekR4mzXZcCdwD8lbSCmUda3lJ8u6QFiZL+EUI88S9LDwNvAsKRFdOd64LBydNyNlPn1DiwHTi7TPweye/T9KHCHpKN6+BnXEE8g2F4HPFf6Xgr8pof6SDoUGLb9Ti/lk2aSapnJ/w0l6C60vXUc+5wOHG77QUmziF1Ap/fRzibgW9UZun3Uv444uS334ycdyRF+kuwdbwCXlUXhxcCP+2xnAXBdPxUlHQccnME+GYsc4SdJkjSEHOEnSZI0hAz4SZIkDSEDfpIkSUPIgJ8kSdIQMuAnSZI0hAz4SZIkDeE/xqnE9ozFfRcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()\n",
    "features = iris.data.T\n",
    "\n",
    "plt.scatter(features[0], features[1], alpha=0.2,\n",
    "            s=100*features[3], c=iris.target, cmap='viridis')\n",
    "plt.xlabel(iris.feature_names[0])\n",
    "plt.ylabel(iris.feature_names[1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> We can see that this scatter plot has given us the ability to simultaneously explore four different dimensions of the data:\n",
    "the (x, y) location of each point corresponds to the sepal length and width, the size of the point is related to the petal width, and the color is related to the particular species of flower.\n",
    "Multicolor and multifeature scatter plots like this can be useful for both exploration and presentation of data.\n",
    "\n",
    "我们可以从上图中看出，可以通过散点图同时展示该数据集的四个不同维度：图中的(x, y)位置代表每个样本的花萼的长度和宽度，散点的大小代表每个样本的花瓣的宽度，而散点的颜色代表一种特定的鸢尾花类型。如上图的多种颜色和多种属性的散点图对于我们分析和展示数据集时都非常有帮助。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ``plot`` Versus ``scatter``: A Note on Efficiency\n",
    "\n",
    "## `plot` 和 `scatter` 对比：性能提醒\n",
    "\n",
    "> Aside from the different features available in ``plt.plot`` and ``plt.scatter``, why might you choose to use one over the other? While it doesn't matter as much for small amounts of data, as datasets get larger than a few thousand points, ``plt.plot`` can be noticeably more efficient than ``plt.scatter``.\n",
    "The reason is that ``plt.scatter`` has the capability to render a different size and/or color for each point, so the renderer must do the extra work of constructing each point individually.\n",
    "In ``plt.plot``, on the other hand, the points are always essentially clones of each other, so the work of determining the appearance of the points is done only once for the entire set of data.\n",
    "For large datasets, the difference between these two can lead to vastly different performance, and for this reason, ``plt.plot`` should be preferred over ``plt.scatter`` for large datasets.\n",
    "\n",
    "除了上面说的`plt.plot`和`plt.scatter`对于每个散点不同属性的支持不同之外，还有别的因素影响对这两个函数的选择吗？对于小的数据集来说，两者并无差别，当数据集增长到几千个点时，`plt.plot`会明显比`plt.scatter`的性能要高。造成这个差异的原因是`plt.scatter`支持每个点使用不同的大小和颜色，因此渲染每个点时需要完成更多额外的工作。而`plt.plot`来说，每个点都是简单的复制另一个点产生，因此对于整个数据集来说，确定每个点的展示属性的工作仅需要进行一次即可。对于很大的数据集来说，这个差异会导致两者性能的巨大区别，因此，对于大数据集应该优先使用`plt.plot`函数。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<!--NAVIGATION-->\n",
    "< [简单的折线图](04.01-Simple-Line-Plots.ipynb) | [目录](Index.ipynb) | [误差可视化](04.03-Errorbars.ipynb) >\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/wangyingsm/Python-Data-Science-Handbook/blob/master/notebooks/04.02-Simple-Scatter-Plots.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
